saikat Gochhait

@siu.edu.in

Assistant Professor Academic Level 12 7th Pay CPC
Symbiosis International Deemed University



                          

https://researchid.co/sgochhait

Dr. Saikat Gochhait teaches at Symbiosis Institute of Digital & Telecom Management, Symbiosis International Deemed University Pune, India and Neurosciences Research Institute-Samara State Medical University, Russia. He is Ph.D and Post-Doctoral Fellow from the UEx, Spain and National Dong Hwa University, Taiwan. He was Awarded DITA and MOFA Fellowship in 2017 and 2018. His research publication with foreign authors is indexed in Scopus, ABDC, and Web of Science. He is a Senior IEEE member.

EDUCATION

Post Doctoral Fellow - Uex, Spain
Post Doctoral Fellow - National Dong Hwa University, Taiwan
PhD - Sambalpur University

RESEARCH INTERESTS

Technology Management
Marketing
Healthcare
Entrepreneurship

FUTURE PROJECTS

Neurosciences

NeuroMarketing


Applications Invited
Collaborators

Entreprenuership

Women Entrepreneurs


Applications Invited
Collaborators
140

Scopus Publications

8724

Scholar Citations

41

Scholar h-index

151

Scholar i10-index

Scopus Publications



  • Incorporating virtual and augmented reality for advanced medical education
    Amitesh Prakash, Saikat Gochhait, Prabakaran Raghavendran, and Tharmalingam Gunasekar

    IGI Global
    Modern simulation models of virtual reality (VR) and augmented reality (AR) are, at present, enhancing medical education. Users can engage structures in real-time 3D interaction using virtual reality. Advanced technologies in haptics, display systems, and motion detection help the user to achieve an experience of realism with interactive features; hence VR is best suited for practical procedures training. As such, applications of VR are found more in surgeries and other interventional procedures. The application of AR allows for the modification or augmentation of the physical environment by combining virtual data and structures with physical objects. It seems useful to have AR applications as an integral part of our knowledge concerning physiological and anatomical processes. Numerous VR and AR applications using various hardware platforms and in diverse settings have been the subject of experiments aiming to prove their realism and didactic value. Some history of VR AR in medicine can be found in this chapter, and some guide ideals and norms rule them.

  • Application of integral transform algorithms for augmenting cryptographic security and performance analysis
    Prakash Chand Thakur, Dinesh Thakur, Tharmalingam Gunasekar, Prabakaran Raghavendran, and Saikat Gochhait

    IGI Global
    This paper presents a cryptographic framework that incorporates the Anuj Transform and the congruence modulo operator to improve data security and allow for efficient information retrieval. The methodology, based on the mathematical properties of the Anuj Transform and its inverse, is used in designing strong encryption and decryption techniques. The additional security of encrypted messages is assured by the incorporation of the congruence modulo operator. Comprehensive analyses are carried out through graphs and evaluations over the principal parameters: encryption precision, computing speed, resistance, scalability. The outcome shows how well the Anuj Transform coupled with the congruence modulo operator can really help to face modern problems within cryptography.

  • Data privacy law in the age of social media
    Saikat Gochhait

    IGI Global
    Although online social platforms are vulnerable to private information leakage, third parties can still do want with your data easily and consent. With Indeed the rapid spread of information today and changed role for social media, people more commonly worry over privacy. India's Digital Personal Data Protection Act 2023 seeks to cope with this risk by strengthening data protection. The legal framework must evolve constantly to guarantee the privacy and dignity of its recipients, permitting properly informed control over personal information in a world increasingly digital all the time.

  • Expecting Solana's Market Volatility with Artificial Neural Networks: A Novel Approach to Cryptocurrency Price Forecasting
    Prabakaran Raghavendran, Tharmalingam Gunasekar, and Saikat Gochhait

    IEEE
    This study examines the emergent interest in accurate Solana price predictions among depositors, buyers, and governmental bodies. Solana, a groundbreaking cryptocurrency known for its reorganized nature, has appealed substantial responsiveness. Applying progressive artificial neural networks (ANN), we aim to projection Solana prices by leveraging their capacity to understand the intricate and impulsive outlines typical of cryptocurrency markets. Our pioneering line of attack encompasses exploring diverse lag conformations over specific time intervals to optimize forecast accuracy and timeliness. Through rigorous validation, focusing on root mean square error as a key performance metric, our ANN model dependably outclasses traditional prediction methods. These findings offer valuable insights for individuals, industries, and governmental bodies directing the intricacies of the cryptocurrency landscape. Furthermore, we introduce an algorithm and provide Python code to determine the execution of our approach for forecasting Solana prices.

  • A New Approach for Solving Fractional Differential Equations Incorporating Ramadan Group Transform and Machine Learning
    Prabakaran Raghavendran, Tharmalingam Gunasekar, and Saikat Gochhait

    European Alliance for Innovation n.o.
    This paper examines various types of fractional differential equations using fractional calculus methods. It extends the classical Frobenius method and introduces key theorems that apply the Ramadan Group transform and other techniques. Additionally, the research incorporates machine learning, specifically neural networks, to solve these equations. The paper demonstrates that machine learning can enhance the solution process through data generation, model design, and optimization. Examples provided illustrate how combining traditional methods with machine learning can effectively solve fractional differential equations.

  • Vision-Based Robust Lane Detection and Tracking System
    M. Vijai, T. Ananth Kumar, P. Kanimozhi, and Saikat Gochhait

    IEEE
    Lane detection and tracking are crucial for modern vehicle navigation systems, especially for ADAS and autonomous vehicles. Traditional methods often fail under adverse conditions such as poor lighting, bad weather, and inconsistent road markings. This paper presents a novel approach using YOLOv5, an advanced object detection model known for its real-time performance and accuracy, to detect lane boundaries directly from images. We improved its robustness in challenging scenarios by adapting YOLOv5 for lane detection and introducing innovative post-processing techniques. These techniques include refining lane predictions, handling occlusions, and reducing noise. Extensive experiments on datasets from various conditions (daytime, nighttime, and adverse weather) show that our method outperforms existing approaches. The proposed YOLOv5-based system offers a promising solution for real-world driving challenges, enhancing the precision and dependability of lane recognition and tracking and positively impacting road safety and autonomous vehicle technologies.

  • Enhancing IoT Cybersecurity Through Innovative Blockchain Solutions
    Rushali Garg, Anuradha S. Kanade, Prabha Kiran, and Saikat Gochhait

    Springer Nature Singapore

  • Your Data, Your Rights: Understanding Consumer Privacy
    Palla Manoj Babu, Ashish Kumar, P. Venkata Subbaiah, V. Mouneswari, Prabha Kiran, and Saikat Gochhait

    Springer Nature Singapore


  • Comparative Long-Term Electricity Forecasting Analysis: A Case Study of Load Dispatch Centres in India
    Saikat Gochhait, Deepak K. Sharma, and Mrinal Bachute

    University of Basrah - College of Engineering
    Accurate long-term load forecasting (LTLF) is crucial for smart grid operations, but existing CNN-based methods face challenges in extracting essential features from electricity load data, resulting in diminished forecasting performance. To overcome this limitation, we propose a novel ensemble model that integrates a feature extraction module, densely connected residual block (DCRB), long short-term memory layer (LSTM), and ensemble thinking. The feature extraction module captures the randomness and trends in climate data, enhancing the accuracy of load data analysis. Leveraging the DCRB, our model demonstrates superior performance by extracting features from multi-scale input data, surpassing conventional CNN-based models. We evaluate our model using hourly load data from Odisha and day-wise data from Delhi, and the experimental results exhibit low root mean square error (RMSE) values of 0.952 and 0.864 for Odisha and Delhi, respectively. This research contributes to a comparative long-term electricity forecasting analysis, showcasing the efficiency of our proposed model in power system management. Moreover, the model holds the potential to sup-port decision making processes, making it a valuable tool for stakeholders in the electricity sector.

  • Preface



  • Green infrastructure for secure and scalable AI-powered prognosis systems
    Priyank Kumar Singh, Mohit Yadav, Saikat Gochhait, and Puwakpitiyage Gayan Dhanushka Wijethilaka

    IGI Global
    The burgeoning field of AI-powered healthcare prognosis offers immense potential, but traditional data center infrastructure creates a significant environmental footprint. This chapter advocates for energy-efficient AI algorithms and hardware alongside renewable energy integration (solar, wind) to minimize reliance on fossil fuels. Robust security measures and privacy-preserving techniques are crucial to protect sensitive patient data used in AI models. Finally, scalable cloud-based infrastructure with containerization and auto-scaling ensures efficient handling of growing data volumes and user demands. By prioritizing these principles, we can create a sustainable and secure future where AI empowers healthcare prognosis, improving patient outcomes for generations to come.

  • Explainable AI (XAI) for green AI-powered disease prognosis
    Shashank Mittal, Priyank Kumar Singh, Saikat Gochhait, and Shubham Kumar

    IGI Global
    Accurate disease prognosis is crucial for improved healthcare outcomes. Artificial intelligence (AI) offers immense potential in this domain, but traditional “black-box” models lack interpretability. This chapter explores the integration of Explainable AI (XAI) with Green AI, a resource-efficient and sustainable approach to AI development. They discuss how XAI can enhance trust in Green AI models for disease prognosis, mitigate potential biases, and promote responsible AI development. They highlight the challenges of balancing interpretability with efficiency and propose future research directions to unlock the full potential of XAI for Green AI-powered disease prognosis. This approach has the potential to revolutionize healthcare by providing accurate, transparent, and environmentally friendly tools for early disease detection and improved patient outcomes.

  • Leveraging artificial intelligence (AI) prediction and green computing for health insights
    Priyank Kumar Singh, Mohit Yadav, Saikat Gochhait, and P. G. S. Amila Jayarathne

    IGI Global
    In this chapter, the authors aim to discuss the significance of integrating AI prediction and green computing in the healthcare field to improve disease diagnosis, treatment, and patient care and minimise the adverse effects on the environment. The methodology employed is the systematic literature review (SLR) approach. The results show that combining green practices with AI prediction enhances the effectiveness and sustainability of the healthcare system. Practical implications are that there is a need for frequent policy updates and practical staff training to improve environmental management. The authors focus on the real-world implications and provide tactical recommendations for healthcare organisations that want to adopt green computing strategies successfully. A strategic perspective should be used with top management's support and all employees' involvement to achieve the organisation's future vision regarding these measures.

  • AI-driven data integration to transform epidemiology
    Shashank Mittal, Priyank Kumar Kumar Singh, Saikat Gochhait, and Shubham Kumar

    IGI Global
    AI is rapidly transforming the field of epidemiology. This chapter explores how AI integrates data analysis, predictive modeling, disease surveillance, and diagnostic tools to significantly improve public health outcomes. AI-driven methodologies enhance diagnostic accuracy, improve disease surveillance efficiency, and aid in developing better predictive models, all of which contribute to improved public health strategies. AI seamlessly integrates with traditional epidemiological approaches, paving the way for a new era in combating infectious diseases. Advancements in AI hold immense promise for the future of public health, with possibilities for real-time disease surveillance, personalized medicine, and more accurate predictive modeling. However, broader adoption and responsible use of AI require careful consideration of ethical issues, data privacy concerns, and collaboration among stakeholders. Ultimately, leveraging AI effectively has the potential to improve public health outcomes, ensure equitable access to healthcare, and enhance global preparedness for health crises.

  • AI-powered clinical trial design with translational bioinformatics
    Shashank Mittal, Priyank Kumar Singh, Saikat Gochhait, Nisha Gaur, and Shubham Kumar

    IGI Global
    Clinical trial design is undergoing a revolution fueled by artificial intelligence (AI) and translational bioinformatics. This chapter explores how AI techniques like machine learning and deep learning are being harnessed to analyze vast datasets of biological and clinical information. By integrating these insights with translational bioinformatics, researchers can identify promising drug candidates, select patients most likely to benefit from treatment, and design more efficient and targeted clinical trials. Real-world examples showcase the application of AI in immuno-oncology patient selection, drug discovery for rare diseases, predicting Alzheimer's trial outcomes, and virtual patient recruitment for cardiovascular studies. While challenges like data quality and ethical considerations exist, AI and translational bioinformatics hold immense promise for accelerating drug development, bringing life-saving therapies to patients faster.

  • Leveraging green AI and big data informatics for personalized disease prediction in clinical decision making
    Mohit Yadav, Priyank Kumar Singh, Saikat Gochhait, Nisha Gaur, and Puwakpitiyage Gayan Dhanushka Wijethilaka

    IGI Global
    This chapter explores the potential of green AI and big data informatics for personalized disease prediction in clinical decision making. Green AI prioritizes efficiency, minimizing computational resources needed to analyze vast healthcare datasets. Big data informatics provides the platform to manage and analyze these datasets for knowledge discovery. This chapter delves into how green AI algorithms optimize resource utilization while big data platforms leverage diverse patient data for more accurate, individual risk assessments. The applications in clinical decision-making encompass early detection, risk stratification, and personalized treatment plans. However, ethical considerations regarding data privacy, bias, and potential job displacement require careful attention. Finally, the future directions highlight advancements in green AI efficiency, explainable models, and integration with other health technologies, paving the way for a future of proactive healthcare and patient empowerment.

  • Preface


  • Designing and implementing a cloud-based content delivery network
    Saikat Gochhait

    IGI Global
    CDN is constituted of three basic components. A content provider is somebody entrusting the URI namespace of the Web objects to be dispersed. The content provider's server contains all such objects. A CDN provider can be some owner party that enables transportation conveniences to content providers to deliver content in a timely and reliable manner. They may employ geographically distributed caching and/or replica servers (surrogates or edge servers) to duplicate content. Together they may form what we call a web cluster. End users are the customers who use content from the content provider's website.


  • Auto-scaling in the cloud
    Saikat Gochhait

    IGI Global
    Cloud computing is gaining momentum as a subscription-oriented paradigm providing on-demand payable access to virtualized IT services and products across the net. It is a breakthrough technology that is offering on-demand access to various services across the network. Auto-scaling, though quite an attractive proposition to customers and naïve cloud service providers, has its own share of issues and challenges. This work was an attempt to classify and appreciate the auto scaling framework while outlining its challenges. Many effective and efficient auto scaling strategies are being deployed by cloud giants like Amazon AWS, Microsoft Azure, etc.

  • URL shortener for web consumption: an extensive and impressive security algorithm
    Saikat Gochhait, Yogesh Singh Rathore, Irina Leonova, Mahima Shanker Pandey, Bal Krishna Saraswat, Santosh Kumar Maurya, Hare Ram Singh, and Nidhi Bansal

    Institute of Advanced Engineering and Science
    <p>URL stands for uniform resource locator are the addresses of the unique resources on the internet. We all need URLs to access any type of resource on the internet, such as any web page, and document. Sometimes URLs can be long, irrelative and unattractive and unable to send sometimes via email. So, for this, we proposed a URL shortener web application based on the Python-Django platform which is fast and makes your long URLs in the shortest form which you can share on social media platforms. It makes all the messy, unattractive URLs short and shareable. Writing paper proposed a premium section in our application that gives access to the customizable URLs and analytics of your shorten URLs. Customizable URLs are the URLs you create by your own keywords. By creating a premium profile with the application, you can create your own URLs by using your own keywords. We have considered security a major part of the application that prevents the short URLs from being hacked or redirected to any advertising website or content. We store all the data related to the URL to show you the best view of your analytics and update it regularly. Main contribution in this field that for web application that provides users with a fast, secure and shortest URL for their using long URLs. Comparatively to other services that are currently available, the application provides superior security, availability, and confidentiality.</p>

RECENT SCHOLAR PUBLICATIONS

  • Digital transformation on national security strategy: A bibliometric analysis
    AL Prianto, AR Amri, G Ilik
    Advanced Research in Intelligence and National Security 1 (1), 1-28 2025

  • Expecting Solana's Market Volatility with Artificial Neural Networks: A Novel Approach to Cryptocurrency Price Forecasting
    P Raghavendran, T Gunasekar, S Gochhait
    2025 4th International Conference on Computing and Information Technology 2025

  • Algorithm, expert, or both? Evaluating the role of feature selection methods on user preferences and reliance
    J Kornowicz, K Thommes
    PloS one 20 (3), e0318874 2025

  • Strengthening resilience against cyberattacks in Moroccan Universities through AHP, TOPSIS, and ITIL v4
    A Chahid, S Ahriz, K El Guemmat, K Mansouri
    Indonesian Journal of Electrical Engineering and Computer Science 37 (3 2025

  • Artificial intelligence and Islamic finance industry: problems and oversight
    I Arsyad, DB Kharisma, J Wiwoho
    International Journal of Law and Management 2025

  • Superposition effect of online news on fintech platforms
    H Xia, S Chen, JZ Zhang, Y Liu
    International Journal of Emerging Markets 20 (3), 1214-1234 2025

  • Optimizing Parkinson’s Disease Detection: Hybrid S-transform-EEG Feature Reduction Through Trajectory Analysis
    MM Afonso, DR Edla, RR Reddy
    SN Computer Science 6 (2), 135 2025

  • Social Media Marketing: Trends and Challenges
    B Rishi, S Bandyopadhyay
    Contemporary Issues in Social Media Marketing, 1-14 2025

  • Machine Learning for Early Breast Cancer Detection
    NA Chowdhury, L Wang, L Gu, M Kaya
    Journal of Engineering and Science in Medical Diagnostics and Therapy 8 (1) 2025

  • Digital Reimbursement Systems in a Student-Run Clinic
    P Nilchian, S Purkayastha, G Thomas, KL Curtis, N Roszkowska, ...
    Journal of Community Health 50 (1), 56-62 2025

  • The Transformation of Marketing Communication in AI-Driven Technology
    KA Saadjad
    SOCIETO COMMUNICATION JOURNAL 2 (2), 1-27 2025

  • Bibliometric Analysis of Studies on Cyber Crimes Between 2000-2023
    M Erdoğan, F Akmeşe
    ADBA Computer Science 2 (1), 19-29 2025

  • Classification of hand rehabilitation surface electromyographic signals based on FLO-SVM classifier
    Y Zhang, Q Hui
    Fourth International Conference on Computer Vision, Application, and 2025

  • White elephant or happiness goodies? The effect of user personality on the perception of digital companionship of smart speaker
    X Ma, Y Huo
    International Journal of Human–Computer Interaction 41 (1), 627-640 2025

  • Beyond pleasure, desire for meaningful consumption and peacefulness from digital entertainment platforms; extending UTAUT2 model with eudemonic motivation and tranquility
    M Kuriakose, G Nagasubramaniyan
    International Journal of Human–Computer Interaction 41 (1), 792-806 2025

  • The Revolution of Customer Experience: Embracing the Digital Realm
    S Pedewad, A Thakur, P Chintale, G Gupta, AP Perumal, S Gochhait
    International Conference on ICT for Sustainable Development, 117-128 2025

  • Application of Integral Transform Algorithms for Augmenting Cryptographic Security and Performance Analysis
    PC Thakur, D Thakur, T Gunasekar, P Raghavendran, S Gochhait
    AI-Driven Healthcare Cybersecurity and Privacy, 187-204 2025

  • Incorporating Virtual and Augmented Reality for Advanced Medical Education
    A Prakash, S Gochhait, P Raghavendran, T Gunasekar
    AI-Powered Systems for Healthcare Diagnostics and Treatment, 329-342 2025

  • Data Privacy Law in the Age of Social Media
    S Gochhait
    Content Moderation in the Age of AI, 85-100 2025

  • Importance of Digital Technologies in Economic Sector of Selected Countries
    AK Singh
    Renewable Energy and the Economic Welfare of Society, 335-370 2025

MOST CITED SCHOLAR PUBLICATIONS

  • Artificial intelligence (AI) applications for marketing: A literature-based study
    A Haleem, M Javaid, MA Qadri, RP Singh, R Suman
    International Journal of Intelligent Networks 3, 119-132 2022
    Citations: 1068

  • Digital transformation in healthcare: technology acceptance and its applications
    AI Stoumpos, F Kitsios, MA Talias
    International journal of environmental research and public health 20 (4), 3407 2023
    Citations: 726

  • Blockchain technology: applications in health care
    S Angraal, HM Krumholz, WL Schulz
    Circulation: Cardiovascular quality and outcomes 10 (9), e003800 2017
    Citations: 634

  • Ai alignment: A comprehensive survey
    J Ji, T Qiu, B Chen, B Zhang, H Lou, K Wang, Y Duan, Z He, J Zhou, ...
    arXiv preprint arXiv:2310.19852 2023
    Citations: 356

  • Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda
    MM Mariani, I Machado, S Nambisan
    Journal of Business Research 155, 113364 2023
    Citations: 247

  • The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field
    G Coorey, GA Figtree, DF Fletcher, VJ Snelson, ST Vernon, D Winlaw, ...
    NPJ digital medicine 5 (1), 126 2022
    Citations: 213

  • Inverting the impacts: Mining, conservation and sustainability claims near the Rio Tinto/QMM ilmenite mine in Southeast Madagascar
    C Seagle
    Green Grabbing: A New Appropriation of Nature, 211-242 2014
    Citations: 162

  • The impact of artificial intelligence on firm performance: an application of the resource-based view to e-commerce firms
    D Chen, JP Esperana, S Wang
    Frontiers in Psychology 13, 884830 2022
    Citations: 148

  • The impact of artificial intelligence on branding: a bibliometric analysis (1982-2019)
    PS Varsha, S Akter, A Kumar, S Gochhait, B Patagundi
    Journal of Global Information Management (JGIM) 29 (4), 221-246 2021
    Citations: 147

  • Artificial intelligence in healthcare: A bibliometric analysis
    BL Jimma
    Telematics and Informatics Reports 9, 100041 2023
    Citations: 114

  • A cross-country analysis of the determinants of customer recommendation intentions for over-the-top (OTT) platforms
    A Yousaf, A Mishra, B Taheri, M Kesgin
    Information & Management 58 (8), 103543 2021
    Citations: 113

  • The effect of top management support on innovation: The mediating role of synergy between organizational structure and information technology
    EM Al Shaar, SA Khattab, RN Alkaied, AQ Manna
    International Review of Management and Business Research 4 (2), 499 2015
    Citations: 113

  • Generative AI in healthcare: advancements in electronic health records, facilitating medical languages, and personalized patient care
    K Nova
    Journal of Advanced Analytics in Healthcare Management 7 (1), 115-131 2023
    Citations: 93

  • AI in consumer behavior
    DC Gkikas, PK Theodoridis
    Advances in Artificial Intelligence-based Technologies: Selected Papers in 2021
    Citations: 93

  • Requirement engineering challenges in agile software development
    A Rasheed, B Zafar, T Shehryar, NA Aslam, M Sajid, N Ali, SH Dar, ...
    Mathematical Problems in Engineering 2021 (1), 6696695 2021
    Citations: 91

  • A systematic review on COVID-19 vaccine strategies, their effectiveness, and issues
    SS Khandker, B Godman, MI Jawad, BA Meghla, TA Tisha, ...
    Vaccines 9 (12), 1387 2021
    Citations: 86

  • Agile scrum issues at large-scale distributed projects: scrum project development at large
    A Khalid, SA Butt, T Jamal, S Gochhait
    International Journal of Software Innovation (IJSI) 8 (2), 85-94 2020
    Citations: 86

  • 6G enabled industrial internet of everything: Towards a theoretical framework
    PK Padhi, F Charrua-Santos
    Applied System Innovation 4 (1), 11 2021
    Citations: 80

  • A comprehensive survey of digital twins in healthcare in the era of metaverse
    M Turab, S Jamil
    BioMedInformatics 3 (3), 563-584 2023
    Citations: 73

  • Metaverse and personal healthcare
    YT Song, J Qin
    Procedia Computer Science 210, 189-197 2022
    Citations: 70

GRANT DETAILS

Department of Science and Industrial Research , Govt of India with Grant of Rs 13,000,00
Ministry of Foreign Affairs, Taiwan with Grant of Rs 12,000,00
University of Deusto, Spain with Research Grant of Rs 2,000,00
University of Extremadura, Spain with Research Grant of Rs 2,000,00
Samara State Medical University, Russia with Research Visit grant of Rs 2,500,00
Symbiosis International Deemed University with Travel and Research Grant of 4,000,000

INDUSTRY EXPERIENCE

IFGL Refractories Ltd